There has been strong demand for the development of an accurate but simple method to assess the freshness of food. In this study, we demonstrated a system to determine food freshness by analyzing the spectral response from a portable visible/near-infrared (VIS/NIR) spectrometer using the Convolutional Neural Network (CNN)-based machine learning algorithm. Spectral response data from salmon, tuna, and beef incubated at 25 °C were obtained every minute for 30 h and then categorized into three states of "fresh", "likely spoiled", and "spoiled" based on time and pH. Using the obtained spectral data, a CNN-based machine learning algorithm was built to evaluate the freshness of experimental objects. In addition, a CNN-based machine learning algorithm with a shift-invariant feature can minimize the effect of the variation caused using multiple devices in a real environment. The accuracy of the obtained machine learning model based on the spectral data in predicting the freshness was approximately 85% for salmon, 88% for tuna, and 92% for beef. Therefore, our study demonstrates the practicality of a portable spectrometer in food freshness assessment.Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic model based on multiple DNNs (MDNNs) and softmax is constructed with the input of fused deep features. https://www.selleckchem.com/products/compound-e.html The proposed method is verified in intelligent failure recognition for automobile final drive to evaluate its performance. A set of contrastive analyses of several intelligent models based on the Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM) and the proposed deep architecture with single sensory signal and multi-channel sensory signals is implemented. The proposed deep architecture of feature extraction and feature fusion on multi-channel sensory signals can effectively recognize the fault patterns of final drive with the best diagnostic accuracy of 95.84%. The results confirm that the proposed method is more robust and effective than other comparative methods in the contrastive experiments.A direct relation between antibiotic use and resistance has been shown at country level. We aim to investigate the association between antibiotic prescribing for patients from individual Dutch primary care practices and antibiotic resistance of bacterial isolates from routinely submitted urine samples from their patient populations. Practices' antibiotic prescribing data were obtained from the Julius Network and related to numbers of registered patients. Practices were classified as low-, middle- or high-prescribers and from each group size-matching practices were chosen. Culture and susceptibility data from submitted urine samples were obtained from the microbiology laboratory. Percentages of resistant isolates, and resistant isolates per 1000 registered patients per year (population resistance) were calculated and compared between the groups. The percentages of resistant Escherichia coli varied considerably between individual practices, but the three prescribing groups' means were very similar. However, as the higher-prescribing practices requested more urine cultures per 1000 registered patients, population resistance was markedly higher in the higher-prescribing groups. This study showed that the highly variable resistance percentages for individual practices were unrelated to antibiotic prescribing levels. However, population resistance (resistant strains per practice population) was related to antibiotic prescribing levels, which was shown to coincide with numbers of urine culture requests. Whether more urine culture requests in the higher-prescribing groups were related to treatment failures, more complex patient populations, or to general practitioners' testing behaviour needs further investigation.Extra-virgin olive oil is regarded as functional food since epidemiological studies and multidisciplinary research have reported convincing evidence that its intake affects beneficially one or more target functions in the body, improves health, and reduces the risk of disease. Its health properties have been related to the major and minor fractions of extra-virgin olive oil. Among olive oil chemical composition, the phenolic fraction has received considerable attention due to its bioactivity in different chronic diseases. The bioactivity of the phenolic compounds could be related to different properties such as antioxidant and anti-inflammatory, although the molecular mechanism of these compounds in relation to many diseases could have different cellular targets. The aim of this review is focused on the extra-virgin olive oil phenolic fraction with particular emphasis on (a) biosynthesis, chemical structure, and influence factors on the final extra-virgin olive oil phenolic composition; (b) structure-antioxidant activity relationships and other molecular mechanisms in relation to many diseases; (c) bioavailability and controlled delivery strategies; (d) alternative sources of olive biophenols. To achieve this goal, a comprehensive review was developed, with particular emphasis on in vitro and in vivo assays as well as clinical trials. This report provides an overview of extra-virgin olive oil phenolic compounds as a tool for functional food, nutraceutical, and pharmaceutical applications.Obesity is a major health problem worldwide and constitutes a sanitary emergency in Mexico, especially childhood obesity. Several studies have proved the relationship between obesity and oxidative stress and the influence of genetic predisposition. This work was aimed to analyze the association of antioxidant enzyme polymorphisms with overweight and obesity in Mexican children and adolescents. A case-control study was performed in 585 children and adolescents aged 3 to 17 years, using two criteria to classify obesity body mass index (BMI) and body fat percentage (BFP). Anthropometric and biochemical measurements were carried out, and malondialdehyde serum levels were determined. Genotyping was done with the Axiom Genome-Wide LAT microarray, including 68 single nucleotide polymorphisms (SNPs) of the glutathione peroxidase (GPX) and paraoxonase (PON) families. We found six haplotypes associated with obesity-two of them (one in GPX3 and the other in GPX5 and GPX6) in a protective direction when obesity was classified by BMI.